A Unified Analysis of Likelihood-based
Estimators in the Plackett--Luce Model
报告专家：韩睿渐 助理教授（香港理工大学）
报告时间：6月17日（星期二）下午16:30-17:30
报告地点：数学学院西202报告厅
报告摘要：The Plackett--Luce model has been extensively used for rank aggregation in social choice theory. A central statistical question in this model concerns estimating the utility vector that governs the model's likelihood. In this paper, we investigate the asymptotic theory of utility vector estimation by maximizing different types of likelihood, such as full, marginal, and quasi-likelihood. Starting from interpreting the estimating equations of these estimators to gain some initial insights, we analyze their asymptotic behavior as the number of compared objects increases. In particular, we establish both uniform consistency and asymptotic normality of these estimators and discuss the trade-off between statistical efficiency and computational complexity. For generality, our results are proven for deterministic graph sequences under appropriate graph topology conditions. These conditions are shown to be informative when applied to common sampling scenarios, such as nonuniform random hypergraph models and hypergraph stochastic block models. Numerical results are provided to support our findings. This is joint work with Yiming Xu.
专家简介：韩睿渐，香港理工大学助理教授。2016年本科毕业于四川大学数学与应用数学试验班，2020年博士毕业于香港科技大学数学系，之后在香港中文大学从事研究型助理教授工作，并在2022年加入香港理工大学。他的研究兴趣主要包括：排序数据分析，在线推断，统计机器学习，大语言模型。其科研成果发表在AOS, JASA, Biometrika, AAP等期刊上。
邀请人：刘伟
